Machine Learning Workflows in Climate Modeling: Design Patterns and Insights from Case Studies
It offers insights for researchers at the interface of data science and climate modeling, but is incremental as it synthesizes existing approaches rather than introducing new methods.
The paper analyzes case studies in machine learning applied to climate modeling, synthesizing workflow design patterns to address challenges like physical consistency and data sparsity, aiming to provide a framework for rigorous and reproducible scientific machine learning.
Machine learning has been increasingly applied in climate modeling on system emulation acceleration, data-driven parameter inference, forecasting, and knowledge discovery, addressing challenges such as physical consistency, multi-scale coupling, data sparsity, robust generalization, and integration with scientific workflows. This paper analyzes a series of case studies from applied machine learning research in climate modeling, with a focus on design choices and workflow structure. Rather than reviewing technical details, we aim to synthesize workflow design patterns across diverse projects in ML-enabled climate modeling: from surrogate modeling, ML parameterization, probabilistic programming, to simulation-based inference, and physics-informed transfer learning. We unpack how these workflows are grounded in physical knowledge, informed by simulation data, and designed to integrate observations. We aim to offer a framework for ensuring rigor in scientific machine learning through more transparent model development, critical evaluation, informed adaptation, and reproducibility, and to contribute to lowering the barrier for interdisciplinary collaboration at the interface of data science and climate modeling.